Evaluating representations by the complexity of learning low-loss
predictors

Evaluating representations by the complexity of learning low-loss predictors

15 September 2020

William F. Whitney

David Brandfonbrener

Papers citing "Evaluating representations by the complexity of learning low-loss predictors"

11 / 11 papers shown

Title
Evaluating Representations with Readout Model Switching Yazhe Li J. Bornschein Marcus Hutter 22 0 0 19 Feb 2023
Sequential Learning Of Neural Networks for Prequential MDL J. Bornschein Yazhe Li Marcus Hutter AI4TS 20 6 0 14 Oct 2022
SynBench: Task-Agnostic Benchmarking of Pretrained Representations using Synthetic Data Ching-Yun Ko Pin-Yu Chen Jeet Mohapatra Payel Das Lucani E. Daniel 19 3 0 06 Oct 2022
FedAvg with Fine Tuning: Local Updates Lead to Representation Learning Liam Collins Hamed Hassani Aryan Mokhtari Sanjay Shakkottai FedML 24 75 0 27 May 2022
Socially Supervised Representation Learning: the Role of Subjectivity in Learning Efficient Representations Julius Taylor Eleni Nisioti Clément Moulin-Frier 14 0 0 20 Sep 2021
Exploring the Latent Space of Autoencoders with Interventional Assays Felix Leeb Stefan Bauer M. Besserve Bernhard Schölkopf DRL 41 17 0 30 Jun 2021
A Closer Look at How Fine-tuning Changes BERT Yichu Zhou Vivek Srikumar 24 63 0 27 Jun 2021
DirectProbe: Studying Representations without Classifiers Yichu Zhou Vivek Srikumar 27 27 0 13 Apr 2021
When Do You Need Billions of Words of Pretraining Data? Yian Zhang Alex Warstadt Haau-Sing Li Samuel R. Bowman 21 136 0 10 Nov 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 226 4,460 0 23 Jan 2020
What you can cram into a single vector: Probing sentence embeddings for linguistic properties Alexis Conneau Germán Kruszewski Guillaume Lample Loïc Barrault Marco Baroni 199 882 0 03 May 2018